Ultra-Wideband Air-to-Ground Propagation Channel Characterization in an Open Area

Khawaja, Wahab; Özdemir, Özgür; Erden, Fatih; Güvenç, İsmail; Matolak, David W.

doi:10.1109/taes.2020.3003104

Cited by 23 publications

(9 citation statements)

References 29 publications

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“…The above studies tackle the channel sensing and allocation problem in CUAV networks by assuming that the radioactive environment is static, and a centralized information aggregator (e.g., a leader UAV) exists. However, more than frequently, a practical CUAV network not only faces a time-varying channel environment, i.e., the UAV-ground communication with multiple antennas will incur a 3D nonstationary geometry-based stochastic channel [12] or the ultra-wideband communication with the Saleh-Valenzuela time-varying statistical channel model [13], but is also deployed in an ad hoc manner. Therefore, it is necessary to develop a distributed sensing-allocation mechanism that causes an affordable level of overhead due to V2V information exchange.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Joint Cooperative Spectrum Sensing and Channel Access in Cognitive UAV Networks

Jiang

Wang

et al. 2022

Sensors

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This paper studies the problem of distributed spectrum/channel access for cognitive radio-enabled unmanned aerial vehicles (CUAVs) that overlay upon primary channels. Under the framework of cooperative spectrum sensing and opportunistic transmission, a one-shot optimization problem for channel allocation, aiming to maximize the expected cumulative weighted reward of multiple CUAVs, is formulated. To handle the uncertainty due to the lack of prior knowledge about the primary user activities as well as the lack of the channel-access coordinator, the original problem is cast into a competition and cooperation hybrid multi-agent reinforcement learning (CCH-MARL) problem in the framework of Markov game (MG). Then, a value-iteration-based RL algorithm, which features upper confidence bound-Hoeffding (UCB-H) strategy searching, is proposed by treating each CUAV as an independent learner (IL). To address the curse of dimensionality, the UCB-H strategy is further extended with a double deep Q-network (DDQN). Numerical simulations show that the proposed algorithms are able to efficiently converge to stable strategies, and significantly improve the network performance when compared with the benchmark algorithms such as the vanilla Q-learning and DDQN algorithms.

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Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Joint Cooperative Spectrum Sensing and Channel Access in Cognitive UAV Networks

Jiang

Wang

et al. 2022

Sensors

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“…In recent years, wideband signals have been promoted in both civilian and military fields; compared with narrowband signals, wideband echo carries more information and has strong anti-interference ability, which is conducive to detection, parameter estimation, and feature extraction in multiple targets [44][45][46], and narrowband DOA estimation technology can also be expanded. Where the famous coherent signal subspace method (CSSM) [47] focuses the data of every frequency on a single reference point, then the processing suitable for narrowband signal is employed; it both adapts to correlated and uncorrelated sources, but preestimation to DOA is needed.…”

Section: Introductionmentioning

confidence: 99%

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Zhen

Guo

2021

Wireless Communications and Mobile Computing

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With the improvement of living standard and the development of science and technology, Internet of Vehicle (IOV) will play an important part in industrial transportation as a main research field of Internet of Things. As a result, it is very necessary to grasp the location of vehicle. However, the traditional single global position system is easily affected by the external environment, so an accessorial locating approach based on wideband direction of arrival (DOA) estimation in intelligent transportation is proposed. First, model the array received signal on the road infrastructure. Then, by means of random forest regression (RFR) in the supervised learning, upper triangle elements of the covariance matrix of each frequency and the actual DOA are, respectively, extracted as the input features and output parameters; thus, the corresponding prediction coefficients are solved by training. After that, the trained RFR model can be used to calculate the final direction using test samples. Finally, these vehicles can be located according to the geometrical relation between the vehicle and the infrastructure. The proposed algorithm is not only suitable for uncorrelated signals but also for uncorrelated and correlated mixed signals without wideband focusing. The simulations show that compared with some sparse recovery algorithm, the prediction accuracy and resolution are effectively improved.

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“…From the existing literatures, a lot of channel models have been studied in indoor or outdoor scenarios [1][2][3][4][5][6][7]. However, there are few studies for the inside of tower structures.…”

Section: Introductionmentioning

confidence: 99%

Channel Propagation Characteristics for the Communications Inside Tower Structure Buildings

Yin¹,

Xie²,

Hao³

et al. 2021

PIER M

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Ultra-Wideband Air-to-Ground Propagation Channel Characterization in an Open Area

Cited by 23 publications

References 29 publications

Multi-Agent Reinforcement Learning for Joint Cooperative Spectrum Sensing and Channel Access in Cognitive UAV Networks

Multi-Agent Reinforcement Learning for Joint Cooperative Spectrum Sensing and Channel Access in Cognitive UAV Networks

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Channel Propagation Characteristics for the Communications Inside Tower Structure Buildings

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